{
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  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "db3ff30a",
   "metadata": {},
   "source": [
    "### 序列数据\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8c223630",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T14:40:33.313434Z",
     "start_time": "2022-07-30T14:40:32.711044Z"
    }
   },
   "outputs": [],
   "source": [
    "# 导包\n",
    "import torch \n",
    "from torch import nn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d13b5f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# d2l \n",
    "# 安装d2l\n",
    "pip install d2l -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acc550b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T14:49:45.061334Z",
     "start_time": "2022-07-30T14:49:44.454957Z"
    }
   },
   "outputs": [],
   "source": [
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "737b672b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T14:50:07.359702Z",
     "start_time": "2022-07-30T14:50:07.341747Z"
    }
   },
   "outputs": [],
   "source": [
    "d2l.plot??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f86ba39c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T14:51:43.678609Z",
     "start_time": "2022-07-30T14:51:43.557933Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"406.885938pt\" height=\"207.83625pt\" viewBox=\"0 0 406.885938 207.83625\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
       " <metadata>\n",
       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2022-07-30T22:51:43.645698</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
       "    <dc:creator>\n",
       "     <cc:Agent>\n",
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    "# 创建序列\n",
    "# 创建1000个点\n",
    "T = 1000\n",
    "time = torch.arange(1, T + 1, dtype=torch.float32)\n",
    "# 创建正弦数据, 加上噪声\n",
    "x = torch.sin(0.01 * time) + torch.normal(0, 0.2, (T, ))\n",
    "d2l.plot(time, [x], xlabel='time', ylabel='x', xlim=[1, 1000], figsize=(6, 3))\n",
    "# plt.plot(time, x)\n",
    "# plt.xlabel('time')\n",
    "# plt.ylabel('x')\n",
    "# plt.xlim(1, 1000)\n",
    "# plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c151d486",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2022-07-30T15:08:08.116842Z"
    }
   },
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   "source": [
    "# X, y 自回归, X, y都是同一组序列数据, 只不过X是历史数据, y是将来数据.\n",
    "tau = 4\n",
    "features = torch.zeros((T - tau, tau))\n",
    "# features.shape\n",
    "# 对features每一列进行赋值\n",
    "for i in range(tau):\n",
    "    features[:, i] = x[i: T - tau + i ]\n",
    "#     print(i, T - tau + i)\n",
    "# 创建labels\n",
    "labels = x[tau:].reshape((-1, 1))\n",
    "\n",
    "batch_size, n_train = 16, 600\n",
    "# 生成dataset\n",
    "train_iter = d2l.load_array((features[:n_train], labels[:n_train]), batch_size, is_train=True)"
   ]
  },
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   "id": "28c14345",
   "metadata": {
    "ExecuteTime": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.4646,  0.4348,  0.9413,  0.6532],\n",
      "        [-0.2371, -0.0082, -0.0489,  0.2562],\n",
      "        [ 0.5977,  0.5688,  0.6985,  0.6446],\n",
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      "        [-0.2963, -0.6558, -0.4904, -0.3865],\n",
      "        [-0.8457, -0.7189, -0.8119, -0.8257],\n",
      "        [ 0.3584,  0.3410,  0.6863,  0.6994],\n",
      "        [-0.7395, -0.7970, -1.2527, -0.6844],\n",
      "        [ 0.6524,  0.2339,  0.5076,  0.7107],\n",
      "        [-0.6906, -0.9549, -0.7995, -1.0752],\n",
      "        [ 0.7686,  0.9459,  0.8712,  1.0941],\n",
      "        [ 0.9967,  0.7686,  0.9459,  0.8712],\n",
      "        [ 0.2394,  0.7628,  0.2060,  0.6524],\n",
      "        [ 0.8662,  0.9664,  0.7705,  0.3965]]) tensor([[ 0.1543],\n",
      "        [-0.1099],\n",
      "        [ 0.6485],\n",
      "        [-0.1386],\n",
      "        [ 1.2666],\n",
      "        [ 0.8811],\n",
      "        [-0.9632],\n",
      "        [-0.7183],\n",
      "        [ 0.4646],\n",
      "        [-0.4004],\n",
      "        [ 0.1256],\n",
      "        [-0.7497],\n",
      "        [ 0.7544],\n",
      "        [ 1.0941],\n",
      "        [ 0.2339],\n",
      "        [ 1.0868]])\n",
      "torch.Size([16, 4]) torch.Size([16, 1])\n"
     ]
    }
   ],
   "source": [
    "for X, y in train_iter:\n",
    "    print(X, y)\n",
    "    print(X.shape, y.shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7d9b91b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T15:06:39.203635Z",
     "start_time": "2022-07-30T15:06:39.188674Z"
    }
   },
   "outputs": [],
   "source": [
    "d2l.load_array??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "123335ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T15:15:17.683520Z",
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   "source": [
    "# 初始化网络权重的函数\n",
    "def init_weights(m):\n",
    "    if type(m) == nn.Linear:\n",
    "        nn.init.xavier_uniform_(m.weight)\n",
    "\n",
    "# 创建模型\n",
    "# 创建一个简单的多层神经网络(感知机)\n",
    "def get_net():\n",
    "    net = nn.Sequential(nn.Linear(4, 10),\n",
    "                       nn.ReLU(),\n",
    "                       nn.Linear(10, 1))\n",
    "    net.apply(init_weights)\n",
    "    return net\n",
    "\n",
    "# 定义损失\n",
    "# mse 均方误差\n",
    "loss = nn.MSELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f6bab5b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T15:19:13.714779Z",
     "start_time": "2022-07-30T15:19:13.524289Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 1 loss: 0.062547\n",
      "epoch 2 loss: 0.054180\n",
      "epoch 3 loss: 0.053983\n",
      "epoch 4 loss: 0.051070\n",
      "epoch 5 loss: 0.050659\n"
     ]
    }
   ],
   "source": [
    "# 开始训练\n",
    "def train(net, train_iter, loss, epochs, lr):\n",
    "    trainer = torch.optim.Adam(net.parameters(), lr)\n",
    "    for epoch in range(epochs):\n",
    "        for X, y in train_iter:\n",
    "            # 梯度归零=\n",
    "            trainer.zero_grad()\n",
    "            l = loss(net(X), y)\n",
    "            l.backward()\n",
    "            trainer.step()\n",
    "        print(f'epoch {epoch + 1}',\n",
    "             f'loss: {d2l.evaluate_loss(net, train_iter, loss):f}')\n",
    "        \n",
    "net = get_net()\n",
    "train(net, train_iter, loss, 5, 0.01)"
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   "cell_type": "code",
   "execution_count": 17,
   "id": "7b7e22d5",
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     "start_time": "2022-07-30T15:18:11.500167Z"
    }
   },
   "outputs": [],
   "source": [
    "d2l.evaluate_loss??"
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  },
  {
   "cell_type": "code",
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   "id": "bebde6a5",
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    "ExecuteTime": {
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     "start_time": "2022-07-30T15:21:45.028104Z"
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   "source": [
    "# 预测\n",
    "onestep_preds = net(features)\n",
    "# 预测结果和真实结果画在一起, 看一下预测的准不准. \n",
    "d2l.plot([time, time[tau:]], [x.detach().numpy(), onestep_preds.detach().numpy()],\n",
    "         'time', 'x', legend=['data', '1-step preds'], xlim=[1, 1000], figsize=(6, 3))"
   ]
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  {
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   "execution_count": 23,
   "id": "0030ec1e",
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     "start_time": "2022-07-30T15:32:16.845360Z"
    },
    "scrolled": true
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   "source": [
    "# 多步预测\n",
    "multistep_preds = torch.zeros(T)\n",
    "# 原始数据\n",
    "multistep_preds[:n_train + tau] = x[: n_train + tau]\n",
    "for i in range(n_train + tau, T):\n",
    "    multistep_preds[i] = net(multistep_preds[i - tau: i].reshape((1, -1)))\n",
    "    \n",
    "# 画图\n",
    "d2l.plot([time, time[tau:], time[n_train + tau:]], \n",
    "        [x.detach().numpy(), onestep_preds.detach().numpy(),\n",
    "         multistep_preds[n_train + tau:].detach().numpy()],\n",
    "        'time', 'x', legend=['data', '1-step preds', 'multistep preds'],\n",
    "        xlim=[0, 1000], figsize=(6, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6aa9e40b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-07-30T15:43:27.936610Z",
     "start_time": "2022-07-30T15:43:27.743128Z"
    }
   },
   "outputs": [
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   "source": [
    "# 观察k步预测的结果\n",
    "max_steps = 64\n",
    "features = torch.zeros((T - tau - max_steps + 1, tau + max_steps))\n",
    "\n",
    "# 给前面tau列赋值\n",
    "for i in range(tau):\n",
    "    features[:, i] = x[i: i + T - tau - max_steps + 1]\n",
    "    \n",
    "# 再给后面tau 到 tau + max_steps列赋值\n",
    "for i in range(tau, tau + max_steps):\n",
    "    features[:, i] = net(features[:, i - tau: i]).reshape(-1)\n",
    "    \n",
    "# k步\n",
    "steps = (1, 4, 16, 64)\n",
    "d2l.plot([time[tau + i -1 : T - max_steps + i] for i in steps],\n",
    "        [features[:, (tau + i - 1)].detach().numpy() for i in steps],\n",
    "        'time', 'x',\n",
    "        legend=[f'{i}-step preds' for i in steps], xlim=[5, 1000],\n",
    "        figsize=(6, 3))"
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